1. Field of the Invention
The invention relates to a method for the processing of digital angiographic images which enables automatic detection of stenoses and comprises at least a step for the identification of pixels or points situated on the central lines of vessels, which step is referred to as a tracking step. The invention is used, for example in digital imaging systems in order to facilitate the detection of anomalies, such as stenoses, on the basis of arteriograms of a human or animal body.
2. Description of the Related Art
Arteriograms are special images for the visualization of blood vessels. Various types of arteriograms can be formed: coronary arteriograms for the imaging of arteries for the muscular tissue of the heart, or myocardium, which arteries together form the coronary tree; peripheral arteriograms for visualization of the feeding of the lower and upper members; cerebral arteriograms. By way of example, coronary arteriograms will be concerned hereinafter. Stenoses are local strictures which are caused by partial or total obstruction occurring in the arteries. In the coronary tree, stenoses seriously hamper the feeding of the myocardium and must be detected by a radiologist on the basis of arteriograms.
The introduction in recent years of digital radiography, combining the use of an X-ray detector providing a real-time image and the digitization of the images, constitutes a major advance in the field of imaging in comparison with conventional radiography. It actually gives access to numerous possibilities offered by the digital image processing techniques.
Other methods of forming angiographic images are also known, for example the methods utilizing magnetic resonance.
The invention, however, depends neither on the method whereby the digital image has been obtained nor on the nature of the objects reproduced therein, but relates exclusively to the processing of the digital image in order to determine the central points and the edge points of the objects represented, provided that these objects constitute sufficiently uniform, contrasting masses on a sufficiently uniform background.
A method for the automated identification of the contours of vessels in coronary arteriograms is known from the publication "Automated Identification of Vessel Contours in Coronary Arteriograms by an adaptive Tracking Algorithm", by Ying Sun, published in IEEE Transactions on Medical Imaging, Vol. 8, No. 1, March 1989. The cited document describes an algorithm which is referred to as "tracking" of the central line of the vessels in order to identify the contours of these vessels in the digitized arteriograms. The algorithm essentially comprises three steps:
1) The identification of points situated on the central line of a vessel. Each point of the central line has three attributes: its position, the direction of a vector parallel to the direction of the vessel segment whereto the point belongs, and the half-width of the vessel at this point. Given a starting point P.sub.k on the central line of the vessel, the algorithm calculates a point P.sub.k+d at a given distance d in the direction of the attribute vector of the starting point P.sub.k. Subsequently, convolution filtering is performed by means of a rectangular filter having a principal orientation perpendicular to said vector, i.e. parallel to the scanning direction at the starting point P.sub.k. PA1 2) The identification of edges of the vessel: the edges of the vessel which correspond to the new point P'.sub.k+d are identified as the position of the points of inflection on a transverse density profile, i.e. perpendicularly to an attribute vector of the point P'.sub.k+d resulting from an updating operation. The half-width of the vessel is thus updated, after which the new point P'.sub.k+d of the central line searched is finally identified. PA1 3) Spatial averaging: this "tracking" operation produces a description of the vessel with N inputs. Each input is characterized by a triplet: position of a point on the central line of the vessel; direction of a vector parallel to the central line in a segment of length d, chosen as a function of the curvature of the vessel; half-width of the vessel at this point.
This filtering operation enables identification of a given point P'.sub.k+d, which is determined by performing the convolution between the density profile along the scanning line passing through the point P.sub.k+d and an ideal density profile of rectangular shape. The convolution results in a vector for which the maximum value is searched, which maximum value relates to a pixel which corresponds to the maximum of the density profile and enables the updating of the new point P'.sub.k+d.
The process is repeated for all points situated on the scanning lines perpendicular to the first attribute vector of the starting point P.sub.k : from k+1 to k+d. This directional vector is maintained so as to be the same for the entire distance d.
In the case of a bifurcation, the process selects the branch of the vessel having the highest density, so that the updated density profile does not exhibit a double peak.
A first technical problem encountered in the processing of arteriograms is the detection of all pathological conditions and the elimination of false alarms.
The pathological conditions to be detected concern not only the stenoses appearing in the form of a local stricture in a vessel which thus simply exhibits a local minimum of the width. The pathological conditions also concern a type of stricture which is referred to as a "step" which appears, in a vessel having a substantially uniform first width, as an abrupt transition to a second width which is smaller than the first width. This type of "step" may signify that a vessel is concerned which referred to as a principal vessel of a first width which branches into two vessels, one of which has a second width and is still visible in the prolongation of the principal vessel, whereas the other vessel is completely occluded as from the point where it is branched from the principal vessel and has disappeared, i.e. become completely invisible in the arteriogram.
The single means of detecting such a completely occluded and hence invisible vessel is to detect the "stepped" stricture in the principal vessel.
The latter pathological condition cannot be recognized by the algorithm described in the cited state of the art. Thus, the known method is not capable of distinguishing the case where a "step" occurs due to the fact that after branching one of the two secondary vessels has completely disappeared, being a serious pathological case, from the non-pathological case where a natural bend occurs in the vessel. As the characteristic "step" shape is the only alarm enabling a radiologist to uncover the occluded vessels, this type of algorithm does not enable the radiologist to detect these pathological conditions which are large in number as well as serious in respect of the condition of the patient.
A second technical problem encountered resides in the implementation of medical imaging systems provided with means for the fully automatic detection of the pathological conditions described above, i.e. the first condition involving local strictures of vessels, and the second condition involving "stepped" strictures. Fully automatic detection is to be understood to mean that the detection of the pathological conditions must be carried out without assistance from an operator.
The formation of angiograms assumes that a patient, usually awake, is injected, for example with a contrast medium by means of a catheter via the femoral artery; subsequently, an operator makes a number of exposures of the coronary tree in the form of a sequence of video images, for example at a rate of 30 images per second. Such a sequence enables the display of several cardiac cycles. The stenoses or strictures described above are the principal anomalies to be detected. However, such detection may be hampered by an unfavorable orientation of the vessels or the course of a background vessel behind a vessel situated in the foreground. Therefore, it is necessary to utilize different projection angles and also to attempt detection of the stenoses in all images of the video sequence for which the concentration of the contrast medium is sufficiently strong to ensure good visibility of the vessels.
Thus, there are many of these images and the radiologist makes his diagnosis while studying these images as they slowly pass by. Thus, there appears to be a need to detect, in advance and automatically, the pathological conditions mentioned above. Psychologically speaking, the radiologist tends to have his attention drawn to the most flagrant pathological conditions, and to ignore given situations which are less visible but which may be more disturbing or serious, from a clinical point of view, for the care of the patient. The radiologist may also let given pathological conditions pass because they appear only in a single image, or in only a few images of the sequence.
Therefore, it is important that the radiologist has available a system for revealing pathological conditions so that his attention is drawn to image areas, or the areas of the single or a few images of the sequence which contain actually the most interesting information which is indispensable for examination. Attention could thus be drawn to the a priori less likely areas nevertheless containing pathological conditions; moreover, his attention could be drawn away from the focusing on a few stenoses which are evident but without major importance from a point of view of further medical treatment.
Such full automation of the detection of the pathological conditions is not achieved by means of the algorithm known from the cited document.